A pan-influenza monoclonal antibody neutralizes H5 strains and prophylactically protects through intranasal administration.

Avian A(H5N1) influenza virus poses an elevated zoonotic threat to humans, and no pharmacological products are currently registered for fast-acting pre-exposure protection in case of spillover leading to a pandemic. Here, we show that an epitope on the stem domain of H5 hemagglutinin is highly conserved and that the human monoclonal antibody CR9114, targeting that epitope, potently neutralizes all pseudotyped H5 viruses tested, even in the rare case of substitutions in its epitope. Further, intranasal administration of CR9114 fully protects mice against A(H5N1) infection at low dosages, irrespective of pre-existing immunity conferred by the quadrivalent seasonal influenza vaccine. These data provide a proof-of-concept for broad, pre-exposure protection against a potential future pandemic using the intranasal administration route. Studies in humans should assess if autonomous administration of a broadly-neutralizing monoclonal antibody is safe and effective and can thus contribute to pandemic preparedness.

SARS-CoV-2 elicits non-sterilizing immunity and evades vaccine-induced immunity: implications for future vaccination strategies.

Neither vaccination nor natural infection result in long-lasting protection against SARS-COV-2 infection and transmission, but both reduce the risk of severe COVID-19. To generate insights into optimal vaccination strategies for prevention of severe COVID-19 in the population, we extended a Susceptible-Exposed-Infectious-Removed (SEIR) mathematical model to compare the impact of vaccines that are highly protective against severe COVID-19 but not against infection and transmission, with those that block SARS-CoV-2 infection. Our analysis shows that vaccination strategies focusing on the prevention of severe COVID-19 are more effective than those focusing on creating of herd immunity. Key uncertainties that would affect the choice of vaccination strategies are: (1) the duration of protection against severe disease, (2) the protection against severe disease from variants that escape vaccine-induced immunity, (3) the incidence of long-COVID and level of protection provided by the vaccine, and (4) the rate of serious adverse events following vaccination, stratified by demographic variables.

Randomized trial of inosine for urate elevation in amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Higher urate levels are associated with improved ALS survival in retrospective studies, however whether raising urate levels confers a survival advantage is unknown. In the Safety of Urate Elevation in Amyotrophic Lateral Sclerosis (SURE-ALS) trial, inosine raised serum urate and was safe and well-tolerated. The SURE-ALS2 trial was designed to assess longer term safety. Functional outcomes and a smartphone application were also explored. METHODS: Participants were randomized 2:1 to inosine (n = 14) or placebo (n = 9) for 20 weeks, titrated to serum urate of 7-8 mg/dL. Primary outcomes were safety and tolerability. Functional outcomes were measured with the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R). Mobility and ALSFRS-R were also assessed by a smartphone application. RESULTS: During inosine treatment, mean urate ranged 5.68-6.82 mg/dL. Treatment-emergent adverse event (TEAE) incidence was similar between groups (p > .10). Renal TEAEs occurred in three (21%) and hypertension in one (7%) of participants randomized to inosine. Inosine was tolerated in 71% of participants versus placebo 67%. Two participants (14%) in the inosine group experienced TEAEs deemed related to treatment (nephrolithiasis); one was a severe adverse event. Mean ALSFRS-R decline did not differ between groups (p = .69). Change in measured home time was similar between groups. Digital and in-clinic ALSFRS-R correlated well. DISCUSSION: Inosine met pre-specified criteria for safety and tolerability. A functional benefit was not demonstrated in this trial designed for safety and tolerability. Findings suggested potential utility for a smartphone application in ALS clinical and research settings.

Exploring the Role of Antiviral Nasal Sprays in the Control of Emerging Respiratory Infections in the Community.

INTRODUCTION: The COVID-19 pandemic has demonstrated that there is an unmet need for the development of novel prophylactic antiviral treatments to control the outbreak of emerging respiratory virus infections. Passive antibody-based immunisation approaches such as intranasal antibody prophylaxis have the potential to provide immediately accessible universal protection as they act directly at the most common route of viral entry, the upper respiratory tract. The need for such products is very apparent for SARS-CoV-2 at present, given the relatively low effectiveness of vaccines to prevent infection and block virus onward transmission. We explore the benefits and challenges of the use of antibody-based nasal sprays prior and post exposure to the virus. METHODS: The classic susceptible-exposed-infectious-removed (SEIR) mathematical model was extended to describe the potential population-level impact of intranasal antibody prophylaxis on controlling the spread of an emerging respiratory infection in the community. RESULTS: Intranasal administration of monoclonal antibodies provides only a short-term protection to the mucosal surface. Consequently, sustained intranasal antibody prophylaxis of a substantial proportion of the population would be needed to contain infections. Post-exposure prophylaxis against the development of severe disease would be essential for the overall reduction in hospital admissions. CONCLUSION: Antibody-based nasal sprays could provide protection against infection to individuals that are likely to be exposed to the virus. Large-scale administration for a long period of time would be challenging. Intranasal antibody prophylaxis alone cannot prevent community-wide transmission of the virus. It could be used along with other protective measures, such as non-pharmaceutical interventions, to bridge the time required to develop and produce effective vaccines, and complement active immunisation strategies.

Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies.

BACKGROUND: Smartphones provide opportunities for musculoskeletal research: they are integrated in participants' daily lives and can be used to collect patient-reported outcomes as well as sensor data from large groups of people. As the field of research with smartphones and smartwatches matures, it has transpired that some of the advantages of this modern technology are in fact double-edged swords. BODY: In this narrative review, we illustrate the advantages of using smartphones for data collection with 18 studies from various musculoskeletal domains. We critically appraised existing literature, debunking some myths around the advantages of smartphones: the myth that smartphone studies automatically enable high engagement, that they reach more representative samples, that they cost little, and that sensor data is objective. We provide a nuanced view of evidence in these areas and discuss strategies to increase engagement, to reach representative samples, to reduce costs and to avoid potential sources of subjectivity in analysing sensor data. CONCLUSION: If smartphone studies are designed without awareness of the challenges inherent to smartphone use, they may fail or may provide biased results. Keeping participants of smartphone studies engaged longitudinally is a major challenge. Based on prior research, we provide 6 actions by researchers to increase engagement. Smartphone studies often have participants that are younger, have higher incomes and high digital literacy. We provide advice for reaching more representative participant groups, and for ensuring that study conclusions are not plagued by bias resulting from unrepresentative sampling. Costs associated with app development and testing, data storage and analysis, and tech support are substantial, even if studies use a 'bring your own device'-policy. Exchange of information on costs, collective app development and usage of open-source tools would help the musculoskeletal community reduce costs of smartphone studies. In general, transparency and wider adoption of best practices would help bringing smartphone studies to the next level. Then, the community can focus on specific challenges of smartphones in musculoskeletal contexts, such as symptom-related barriers to using smartphones for research, validating algorithms in patient populations with reduced functional ability, digitising validated questionnaires, and methods to reliably quantify pain, quality of life and fatigue.

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies.

BACKGROUND: Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients' cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term. OBJECTIVE: The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis. METHODS: We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan-Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer. RESULTS: Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%). CONCLUSIONS: These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients' progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful. TRIAL REGISTRATION: ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711.

Heterogeneity in the association between weather and pain severity among patients with chronic pain: a Bayesian multilevel regression analysis.

INTRODUCTION: Previous studies on the association between weather and pain severity among patients with chronic pain have produced mixed results. In part, this inconsistency may be due to differences in individual pain responses to the weather. METHODS: To test the hypothesis that there might be subgroups of participants with different pain responses to different weather conditions, we examined data from a longitudinal smartphone-based study, Cloudy with a Chance of Pain, conducted between January 2016 and April 2017. The study recruited more than 13,000 participants and recorded daily pain severity on a 5-point scale (range: no pain to very severe pain) along with hourly local weather data for up to 15 months. We used a Bayesian multilevel model to examine the weather-pain association. RESULTS: We found 1 in 10 patients with chronic pain were sensitive to the temperature, 1 in 25 to relative humidity, 1 in 50 to pressure, and 3 in 100 to wind speed, after adjusting for age, sex, belief in the weather-pain association, mood, and activity level. The direction of the weather-pain association differed between people. Although participants seem to be differentially sensitive to weather conditions, there is no definite indication that participants' underlying pain conditions play a role in weather sensitivity. CONCLUSION: This study demonstrated that weather sensitivity among patients with chronic pain is more apparent in some subgroups of participants. In addition, among those sensitive to the weather, the direction of the weather-pain association can differ.

Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study.

BACKGROUND: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. OBJECTIVE: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. METHODS: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants' time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. RESULTS: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. CONCLUSIONS: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS.

INTRODUCTION: Passive data from smartphone sensors may be useful for health-care research. Our aim was to use the coronavirus disease-2019 (COVID-19) pandemic as a positive control to assess the ability to quantify behavioral changes in people with amyotrophic lateral sclerosis (ALS) from smartphone data. METHODS: Eight participants used the Beiwe smartphone application, which passively measured their location during the COVID-19 outbreak. We used an interrupted time series to quantify the effect of the US state of emergency declaration on daily home time and daily distance traveled. RESULTS: After the state of emergency declaration, median daily home time increased from 19.4 (interquartile range [IQR], 15.4-22.0) hours to 23.7 (IQR, 22.2-24.0) hours and median distance traveled decreased from 42 (IQR, 13-83) km to 3.7 (IQR, 1.5-10.3) km. The participant with the lowest functional ability changed behavior earlier. This participant stayed at home more and traveled less than the participant with highest functional ability, both before and after the state of emergency. DISCUSSION: We provide evidence that smartphone-based digital phenotyping can quantify the behavior of people with ALS. Although participants spent large amounts of time at home at baseline, the COVID-19 state of emergency declaration reduced their mobility further. Given participants' high level of daily home time, it is possible that their exposure to COVID-19 could be less than that of the general population.

Increase in Suicidal Thinking During COVID-19.

There is concern that the COVID-19 pandemic may cause increased risk of suicide. In the current study, we tested whether suicidal thinking has increased during the COVID-19 pandemic and whether such thinking was predicted by increased feelings of social isolation. In a sample of 55 individuals recently hospitalized for suicidal thinking or behaviors and participating in a 6-month intensive longitudinal smartphone monitoring study, we examined suicidal thinking and isolation before and after the COVID-19 pandemic was declared a national emergency in the United States. We found that suicidal thinking increased significantly among adults (odds ratio [OR] = 4.01, 95% confidence interval [CI] = [3.28, 4.90], p < .001) but not adolescents (OR = 0.84, 95% CI = [0.69, 1.01], p = .07) during the onset of the COVID-19 pandemic. Increased feelings of isolation predicted suicidal thinking during the pandemic phase. Given the importance of social distancing policies, these findings support the need for digital outreach and treatment.

Development of a Mobile Digital Manikin to Measure Pain Location and Intensity.

Painful conditions are prevalent and substantially contribute to disability worldwide. Digital manikins are body-shaped drawings to facilitate self-reporting of pain. Some of them have been validated, but without allowing for recording of location-specific pain intensity and for use on a smartphone. This paper describes the initial development of a digital pain manikin to support self-reporting of pain location and location-specific intensity using people's own mobile device. Subsequently, we conducted reliability and usability tests with eight researchers and seven patient representatives. Test-retest reliability depended on the manikin's level of detail, but was generally high with most intraclass correlation coefficients âLe0.70 and all similarity coefficients âLe0.50. Participants found the manikin easy to use, but suggested clearer orientation (front/back, certain body locations) and would value additional feedback and diary functions. We will address these issues in the next version of the manikin before conducting a validation study.

Are weather conditions associated with chronic musculoskeletal pain? Review of results and methodologies.

Many people believe that weather influences chronic musculoskeletal pain. Previous studies on this association are narratively reviewed, with particular focus on comparing methodologies and summarising study findings in light of study quality. We searched 5 databases (Medline, Embase, Web of Science, PsycINFO, and Scopus) for observational studies on the association between weather variables and self-reported musculoskeletal pain severity. Of 4707 located articles, 43 were eligible for inclusion. The majority (67%) found some association between pain and a weather variable. Temperature, atmospheric pressure, relative humidity, and precipitation were most often investigated. For each weather variable, some studies found an association with pain (in either direction), and others did not. Most studies (86%) had a longitudinal study design, usually collecting outcome data for less than a month, from fewer than 100 participants. Most studies blinded participants to study aims but were at a high risk of misclassification of exposure and did not meet reporting requirements. Pain severity was most often self-reported (84%) on a numeric rating scale or visual analog scale. Weather data were collected from local weather stations, usually on the assumption that participants stayed in their home city. Analysis methods, preparation of weather data, and adjustment for covariates varied widely between studies. The association between weather and pain has been difficult to characterise. To obtain more clarity, future studies should address 3 main limitations of the previous literature: small sample sizes and short study durations, misclassification of exposure, and approach to statistical analysis (specifically, multiple comparisons and adjusting for covariates).

Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study.

BACKGROUND: Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. OBJECTIVE: This study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA). METHODS: For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. RESULTS: A total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day. CONCLUSIONS: This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10238.

How the weather affects the pain of citizen scientists using a smartphone app.

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period. The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity. This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts.

Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.

OBJECTIVE: The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study.

BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research. OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies. METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance. RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019. CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.

Consumer Smartwatches for Collecting Self-Report and Sensor Data: App Design and Engagement.

Longitudinal data from patients' natural environments would benefit chronic disease care, yet most devices cannot collect sensor data alongside patient-reported outcomes. Here we describe Koalap, a consumer cellular smartwatch application that collects patient-reported outcomes alongside physical activity data from various sensors. Additionally, we show preliminary results indicating high engagement of our 26 participants with knee osteoarthritis. Our future work will show whether data collection with consumer smartwatches is feasible in terms of user engagement, acceptability, data quality and consistency.

Solving Interoperability in Translational Health. Perspectives of Students from the International Partnership in Health Informatics Education (IPHIE) 2016 Master Class.

BACKGROUND: In the summer of 2016 an international group of biomedical and health informatics faculty and graduate students gathered for the 16th meeting of the International Partnership in Health Informatics Education (IPHIE) masterclass at the University of Utah campus in Salt Lake City, Utah. This international biomedical and health informatics workshop was created to share knowledge and explore issues in biomedical health informatics (BHI). OBJECTIVE: The goal of this paper is to summarize the discussions of biomedical and health informatics graduate students who were asked to define interoperability, and make critical observations to gather insight on how to improve biomedical education. METHODS: Students were assigned to one of four groups and asked to define interoperability and explore potential solutions to current problems of interoperability in health care. RESULTS: We summarize here the student reports on the importance and possible solutions to the "interoperability problem" in biomedical informatics. Reports are provided from each of the four groups of highly qualified graduate students from leading BHI programs in the US, Europe and Asia. CONCLUSION: International workshops such as IPHIE provide a unique opportunity for graduate student learning and knowledge sharing. BHI faculty are encouraged to incorporate into their curriculum opportunities to exercise and strengthen student critical thinking to prepare our students for solving health informatics problems in the future.